What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Image quality, Computer vision, Image processing and Distortion.
His Artificial intelligence research incorporates themes from Machine learning and Pattern recognition.
Zhou Wang has researched Image quality in several fields, including Structural similarity, Video quality, Data mining and Human visual system model.
In Computer vision, Zhou Wang works on issues like Entropy, which are connected to Salience, Visual saliency, Novelty and Side information.
His work on Digital image processing and Digital image as part of general Image processing study is frequently connected to Mean squared error, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The concepts of his Distortion study are interwoven with issues in Feature, Contrast, Mean opinion score, Representation and Perceptual Distortion.
His most cited work include:
- A universal image quality index (3978 citations)
- Multiscale structural similarity for image quality assessment (2182 citations)
- Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures (1724 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Image quality, Pattern recognition and Image processing.
The Artificial intelligence study combines topics in areas such as Distortion and Video quality.
His study looks at the relationship between Computer vision and topics such as Algorithm, which overlap with Lagrange multiplier and Theoretical computer science.
His Image quality study combines topics in areas such as Transform coding, Structural similarity, Machine learning and Data mining.
His Pattern recognition research is multidisciplinary, incorporating elements of Normalization and Representation.
His studies deal with areas such as Information distance and Kolmogorov complexity as well as Image processing.
He most often published in these fields:
- Artificial intelligence (67.91%)
- Computer vision (49.25%)
- Image quality (36.19%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (67.91%)
- Image quality (36.19%)
- Encoder (6.72%)
In recent papers he was focusing on the following fields of study:
Zhou Wang mainly investigates Artificial intelligence, Image quality, Encoder, Cancer research and Computer vision.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.
The various areas that Zhou Wang examines in his Pattern recognition study include Learning to rank, Image processing, Digital image, Pixel and Robustness.
His Image quality research includes elements of Brightness, Distortion and Field.
His research in Computer vision intersects with topics in Resolution and Radiance.
Zhou Wang has included themes like Transform coding, Multimedia and Video quality in his Data compression study.
Between 2018 and 2021, his most popular works were:
- Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network (59 citations)
- Group Maximum Differentiation Competition: Model Comparison with Few Samples (20 citations)
- Deep Guided Learning for Fast Multi-Exposure Image Fusion (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Zhou Wang mainly focuses on Artificial intelligence, Image quality, Distortion, Quality of experience and Pattern recognition.
His Artificial intelligence study deals with Machine learning intersecting with Benchmark.
His Field research extends to Image quality, which is thematically connected.
His work carried out in the field of Distortion brings together such families of science as Transform coding, Point cloud, Data mining and Gaussian noise.
His Quality of experience research includes themes of Multimedia and Human visual system model.
His Pattern recognition research is multidisciplinary, relying on both Image, Deep learning, Deep neural networks and Quality assessment.
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